模糊自适应控制那本书后的所有仿真代码,对初学simulink的同学很有帮助

				%Discrete Reaching Law VSS Control based on Kalman Filter
				clear all;
				close all;
				
				a=25;b=133;
				
				ts=0.001;
				A1=[0,1;0,-a];
				B1=[0;b];
				C1=[1,0];
				D1=0;
				[A,B,C,D]=c2dm(A1,B1,C1,D1,ts,'z');
				
				x=[-0.5;-0.5];
				r_1=0;r_2=0;
				
				Q=10;           %Covariances of w
				Rm=10;          %Covariances of v
				P=B*Q*B';       %Initial error covariance
				
				for k=1:1:2000
				   time(k)=k*ts;
				   
				   r(k)=0.5*sin(1*2*pi*k*ts);
				   c=30;eq=150;q=300;
				   Ce=[c,1];
				   
				%Using Waitui method   
				   dr(k)=(r(k)-r_1)/ts;
				   dr_1=(r_1-r_2)/ts;
				   r1(k)=2*r(k)-r_1;
				   dr1(k)=2*dr(k)-dr_1;
				  
				   R=[r(k);dr(k)];
				   R1=[r1(k);dr1(k)];
				   
				   E=R-x;
				   e(k)=E(1);
				   de(k)=E(2);
				   
				   s(k)=Ce*E;
				   ds(k)=-eq*ts*sign(s(k))-q*ts*s(k);
				   
				   u(k)=inv(Ce*B)*(Ce*R1-Ce*A*x-s(k)-ds(k));
					wn(k)=rands(1);       %Process noise on u
					u(k)=u(k)+wn(k);
				   
				   x=A*x+B*u(k);
				   v(k)=0.015*rands(1);  %Measurement noise on y
				   yv(k)=C*x+v(k);
				
				 M=1;
				 if M==1        %Kalman Filter
				    Mn=P*C'/(C*P*C'+Rm);
				    P=A*P*A'+B*Q*B'; 
				    P=(eye(2)-Mn*C)*P;
				    x=A*x+Mn*(yv(k)-C*A*x);
				    ye(k)=C*x;
				 elseif M==2    %No Filter
					 ye(k)=yv(k); 
				    x(1)=ye(k);
				 end
				 
				 
				%Update Parameters
				r_2=r_1;
				r_1=r(k);
				end
				
				figure(1);
				subplot(211);
				plot(time,yv,'b');
				xlabel('Time(s)');ylabel('yv');
				subplot(212);
				plot(time,ye,'r');
				xlabel('Time(s)');ylabel('ye');
				
				figure(2);
				plot(time,r,'r',time,ye,'b');
				xlabel('Time(second)');ylabel('position tracking');
				figure(3);
				plot(time,s,'r');
				xlabel('Time(second)');ylabel('Switch function s');
				figure(4);
				plot(e,de,'r',e,-c*e,'b');
				xlabel('e');ylabel('de');
				figure(5);
				plot(time,u,'r');
				xlabel('Time(second)');ylabel('u');